作者: scrutiny 時(shí)間: 2025-3-21 20:55 作者: Glucose 時(shí)間: 2025-3-22 02:39 作者: 緯度 時(shí)間: 2025-3-22 05:45
978-3-031-73228-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Cholecystokinin 時(shí)間: 2025-3-22 09:12 作者: 社團(tuán) 時(shí)間: 2025-3-22 16:46 作者: 社團(tuán) 時(shí)間: 2025-3-22 17:51 作者: 無(wú)能的人 時(shí)間: 2025-3-23 00:46 作者: foliage 時(shí)間: 2025-3-23 02:14 作者: 鳥(niǎo)籠 時(shí)間: 2025-3-23 05:58
https://doi.org/10.1007/978-3-662-49885-9signers focus more on structural plausibility, .., no missing component, rather than visual artifacts, .., noises or blurriness. Meanwhile, commonly used metrics such as Fréchet Inception Distance (FID) may not evaluate accurately because they are sensitive to visual artifacts and tolerant to semant作者: objection 時(shí)間: 2025-3-23 11:21
Martina D?bele,Ute Becker,Brigitte Glückn a controlled environment. We introduce a weakly-supervised method that avoids such requirements by leveraging fundamental principles well-established in the understanding of the human hand’s unique structure and functionality. Specifically, we systematically study hand knowledge from different sou作者: 澄清 時(shí)間: 2025-3-23 17:28 作者: 誓言 時(shí)間: 2025-3-23 18:31 作者: 無(wú)政府主義者 時(shí)間: 2025-3-23 23:47 作者: 有節(jié)制 時(shí)間: 2025-3-24 04:15 作者: 開(kāi)玩笑 時(shí)間: 2025-3-24 08:44
https://doi.org/10.1007/978-3-476-05116-5ften work with crops around the object of interest and ignore the location of the object in the camera’s field of view. We note that ignoring this location information further exaggerates the inherent ambiguity in making 3D inferences from 2D images and can prevent models from even fitting to the tr作者: 乞討 時(shí)間: 2025-3-24 14:11 作者: gusher 時(shí)間: 2025-3-24 16:16 作者: 禍害隱伏 時(shí)間: 2025-3-24 19:49 作者: 我不怕?tīng)奚?nbsp; 時(shí)間: 2025-3-24 23:35 作者: 溫室 時(shí)間: 2025-3-25 05:38 作者: condescend 時(shí)間: 2025-3-25 07:44 作者: Priapism 時(shí)間: 2025-3-25 15:36
W. Steggemann,C. Krabbe-Steggemanntest dataset with a limited annotation budget. Previous approaches relied on deep ensemble models to identify highly informative instances for labeling, but fell short in dense recognition tasks like segmentation and object detection due to their high computational costs. In this work, we present Me作者: peak-flow 時(shí)間: 2025-3-25 18:14 作者: 誘惑 時(shí)間: 2025-3-25 22:03 作者: 浪費(fèi)物質(zhì) 時(shí)間: 2025-3-26 02:35 作者: Charitable 時(shí)間: 2025-3-26 06:41
https://doi.org/10.1007/978-3-662-47312-2age without relying on reference captions, bridging the gap between human judgment and machine-generated image captions. Experiments spanning several datasets demonstrate that our proposal achieves state-of-the-art results compared to existing reference-free evaluation scores. Our source code and trained models are publicly available at: ..作者: probate 時(shí)間: 2025-3-26 09:51 作者: INTER 時(shí)間: 2025-3-26 15:39 作者: 誘拐 時(shí)間: 2025-3-26 19:16
ABDM und Schwangerschaftshypertonieout manually tuning the perturbation parameters; and a novel application of Gumbel-sigmoid reparameterization for efficiently learning Bernoulli-distributed binary masks under continuous optimization. Our experiments on problematic region detection and faithfulness tests demonstrate our method’s superiority over state-of-the-art UA methods.作者: 發(fā)源 時(shí)間: 2025-3-26 22:35 作者: THE 時(shí)間: 2025-3-27 01:11 作者: Affable 時(shí)間: 2025-3-27 08:24
,Enhancing Plausibility Evaluation for?Generated Designs with?Denoising Autoencoder,l metric Fréchet Denoised Distance (FDD). We experimentally test our FDD, FID and other state-of-the-art metrics on multiple datasets, .., BIKED, Seeing3DChairs, FFHQ and ImageNet. Our FDD can effectively detect implausible structures and is more consistent with structural inspections by human experts. Our source code is publicly available at ..作者: 發(fā)電機(jī) 時(shí)間: 2025-3-27 13:31 作者: OREX 時(shí)間: 2025-3-27 17:03
Optimization-Based Uncertainty Attribution Via Learning Informative Perturbations,out manually tuning the perturbation parameters; and a novel application of Gumbel-sigmoid reparameterization for efficiently learning Bernoulli-distributed binary masks under continuous optimization. Our experiments on problematic region detection and faithfulness tests demonstrate our method’s superiority over state-of-the-art UA methods.作者: intrude 時(shí)間: 2025-3-27 18:35
,Context-Aware Action Recognition: Introducing a?Comprehensive Dataset for?Behavior Contrast,lso extends to everyday situations like basketball, underscoring the task’s broad relevance. By evaluating leading techniques on this dataset, we aim to unearth valuable insights, pushing the boundaries of action understanding in both industrial and everyday contexts.作者: GROSS 時(shí)間: 2025-3-27 22:51 作者: Jejune 時(shí)間: 2025-3-28 05:03
https://doi.org/10.1007/978-3-476-05116-5rops in the image and camera intrinsics. Experiments on three popular 3D-from-a-single-image benchmarks: depth prediction on NYU, 3D object detection on KITTI & nuScenes, and predicting 3D shapes of articulated objects on ARCTIC, show the benefits of KPE.作者: 小故事 時(shí)間: 2025-3-28 07:53 作者: heterogeneous 時(shí)間: 2025-3-28 11:35
Mark Docherty,Andrew Carson,Matthew Ward on 11 benchmark downstream classification tasks with 4 popular pre-trained models. Our method is . better than the deep features without SeA on average. Moreover, compared to the expensive fine-tuning that is expected to give good performance, SeA shows a comparable performance on 6 out of 11 tasks作者: Pander 時(shí)間: 2025-3-28 16:19 作者: 用手捏 時(shí)間: 2025-3-28 19:20
Paresh Wankhade,Kevin Mackway-Jonespagation, presenting an efficient solution to enhance adversarial robustness. Our comprehensive evaluation conducted across standard datasets, demonstrates that our DR splitting-based model not only improves adversarial robustness but also achieves this with remarkable efficiency compared to various作者: narcissism 時(shí)間: 2025-3-28 23:31 作者: 鑒賞家 時(shí)間: 2025-3-29 03:32 作者: Limpid 時(shí)間: 2025-3-29 08:41
Atemstimulierende Einreibung (ASE),ned features provides significant insight into the downstream utility of trained networks. Informed by this analysis, we propose a simple geometric regularization strategy, which improves the transferability of supervised pre-training. Our work thus sheds light onto both the specific challenges of p作者: foodstuff 時(shí)間: 2025-3-29 13:17 作者: 諂媚于性 時(shí)間: 2025-3-29 19:21
https://doi.org/10.1007/978-3-476-05116-5, a system for 3D hand pose estimation in everyday egocentric images. Zero-shot evaluation on?4 diverse datasets (H2O, AssemblyHands, Epic-Kitchens, Ego-Exo4D) demonstrate?the effectiveness of our approach across 2D and 3D metrics, where we beat?past methods by 7.4% – 66%. In system level comparison作者: Cholesterol 時(shí)間: 2025-3-29 22:12 作者: BILL 時(shí)間: 2025-3-30 03:14
Bauliche Voraussetzungen und Hygienetrue topological signals and become robust to noise. Extensive experiments on public histopathology image datasets show the superiority of our method, especially on topology-aware evaluation metrics. Code is available at ..作者: 策略 時(shí)間: 2025-3-30 05:43
Bauliche Voraussetzungen und Hygieness of AdaSense in reconstructing facial images from a small number of measurements. Furthermore, we apply AdaSense for active acquisition of medical images in the domains of magnetic resonance imaging (MRI) and computed tomography (CT), highlighting its potential for tangible real-world acceleration作者: 紅潤(rùn) 時(shí)間: 2025-3-30 11:51
Bauliche Voraussetzungen und Hygienees. To address this, we introduce the concept of a meta-calibrator that performs uncertainty calibration for NeRFs with a single forward pass without the need for holding out any images from the target scene. Our meta-calibrator is a neural network that takes as input the NeRF images and uncalibrate作者: DALLY 時(shí)間: 2025-3-30 12:29
W. Steggemann,C. Krabbe-Steggemanntrates consistent and substantial performance improvements over five popular benchmarks compared with state-of-the-art methods. Notably, on the CityScapes dataset, MetaAT achieves a 1.36% error rate in performance estimation using only 0.07% of annotations, marking a . improvement over existing stat作者: 外觀 時(shí)間: 2025-3-30 16:41
,SeA: Semantic Adversarial Augmentation for?Last Layer Features from?Unsupervised Representation Lea on 11 benchmark downstream classification tasks with 4 popular pre-trained models. Our method is . better than the deep features without SeA on average. Moreover, compared to the expensive fine-tuning that is expected to give good performance, SeA shows a comparable performance on 6 out of 11 tasks作者: osculate 時(shí)間: 2025-3-30 22:15
,Unlocking the?Potential of?Federated Learning: The Symphony of?Dataset Distillation via?Deep Generaly minimizing resource utilization. We substantiate our claim with a theoretical analysis, demonstrating the asymptotic resemblance of the process to the hypothetical ideal of completely centralized training on a heterogeneous dataset. Empirical evidence from our comprehensive experiments indicates 作者: ticlopidine 時(shí)間: 2025-3-31 04:26
,Rethinking Fast Adversarial Training: A Splitting Technique to?Overcome Catastrophic Overfitting,pagation, presenting an efficient solution to enhance adversarial robustness. Our comprehensive evaluation conducted across standard datasets, demonstrates that our DR splitting-based model not only improves adversarial robustness but also achieves this with remarkable efficiency compared to various作者: assail 時(shí)間: 2025-3-31 05:21 作者: Carbon-Monoxide 時(shí)間: 2025-3-31 11:31 作者: Binge-Drinking 時(shí)間: 2025-3-31 17:26 作者: 侵略主義 時(shí)間: 2025-3-31 18:12 作者: 杠桿 時(shí)間: 2025-4-1 01:38
,3D Hand Pose Estimation in?Everyday Egocentric Images,, a system for 3D hand pose estimation in everyday egocentric images. Zero-shot evaluation on?4 diverse datasets (H2O, AssemblyHands, Epic-Kitchens, Ego-Exo4D) demonstrate?the effectiveness of our approach across 2D and 3D metrics, where we beat?past methods by 7.4% – 66%. In system level comparison作者: Aromatic 時(shí)間: 2025-4-1 01:54 作者: 鞭子 時(shí)間: 2025-4-1 06:33
,Semi-supervised Segmentation of?Histopathology Images with?Noise-Aware Topological Consistency,true topological signals and become robust to noise. Extensive experiments on public histopathology image datasets show the superiority of our method, especially on topology-aware evaluation metrics. Code is available at ..作者: overrule 時(shí)間: 2025-4-1 10:35 作者: 分期付款 時(shí)間: 2025-4-1 18:17 作者: HACK 時(shí)間: 2025-4-1 21:07
,MetaAT: Active Testing for?Label-Efficient Evaluation of?Dense Recognition Tasks,trates consistent and substantial performance improvements over five popular benchmarks compared with state-of-the-art methods. Notably, on the CityScapes dataset, MetaAT achieves a 1.36% error rate in performance estimation using only 0.07% of annotations, marking a . improvement over existing stat作者: 單調(diào)性 時(shí)間: 2025-4-1 23:32 作者: 種子 時(shí)間: 2025-4-2 06:18
,Unlocking the?Potential of?Federated Learning: The Symphony of?Dataset Distillation via?Deep Generarformed at the client level, to attempt to mitigate some of these challenges. In this paper, we propose a highly efficient FL dataset distillation framework on the . side, significantly reducing both the computational and communication demands on local devices while enhancing the clients’ privacy. U
